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"The combined results of several people working together is often much more effective than could be that of an
individual scientist working alone." - John Bardeen, only person to win Nobel Prize in physics twice.

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The Problem and Its Background

Introduction

With the advancement of technology and the decline of manual labor, humans try to improve their quality of life using any innovation that they can think of. Technology makes our lives easier and more efficient. In turn, efficiency implies that we can allot the saved time for other tasks. For example, the evolution of buttons to touch screen. This evolution saved precious time by introducing dynamic menus, faster input, and a lot of flexibility. However, evolution does not stop there. With the introduction of electromyography (EMG), or the technique of evaluating and recording muscle activity through either a needle (intramuscular) or electrodes on muscles (surface), another form of input was made. It’s unclear if it provides a substantial increase in efficiency compared to traditional touch screens. However, having different options for certain situations are favorable. Other forms of input are: speech recognition, eye gaze trackers, and computer vision. These three other forms of input however, are unfavorable in certain situations; electromyography may be used for those situations. Surface EMG may be preferred over intramuscular EMG because it’s too professional and expensive. One device that’s able to conduct surface EMG is the Myo armband.

Background of the Study

Electromyography or EMG measures the electrical activity that occurs when the muscle’s nerve is simulated (Elamvazuthi 2015). The most commonly known is Surface EMG in which there is a device that is placed outside of the body near the part where the muscle, that is going to be tested, is located. EMG makes it relevant as an alternate input method rather than touch screens and eye recognition because of the different situations and adequacy of the method. According to Saponas (Saponas 2010), EMG input allows people to use their hands as input without sensing any technological interface on their hands. EMG which has been used with computers are called muscle-computer interfaces. In an experiment conducted by Saponas (Saponas 2008), 10 sensors were used that is wrapped around the forearm which the detection showed reliable interpretations of the muscle movements.

EMG has also been used in the gaming industry to replace traditional components such as keyboards and joysticks. In a study by Wheeler and Jorgensen (Wheeler 2003) they have developed a wearable dry-electrode sleeve device that is tested using a virtual numpad that would distinguish the keys that were hit. Another study according to Jayarathne et al (Jayarathne 2015) they have developed a muscle effort indicator application that they also implemented in the flappy bird game. This has proven that the EMG can replace the physical components in the gaming industry.

However with all these study which has gained a high accuracy there is not a study that uses two EMG controllers asynchronously in playing a game. In the study conducted by Armiger et al (Armiger 2008) they used a surface EMG to train the machine on detecting the finger movements that is going to be inputted on the program. This study was aimed for people with upper-extremity amputees in which instead of using the guitar as an input, they used EMG. The results of the study were relatively low rather than those achieved in a physical controller.